Thoracic pain is a shared symptom among gastrointestinal diseases, muscle pain, emotional disorders, and the most deadly: Cardiovascular diseases. Due to the limited space in the emergency department, it is important to identify when thoracic pain is of cardiac origin, since being a symptom of CVD (Cardiovascular Disease), the attention to the patient must be immediate to prevent irreversible injuries or even death. Artificial intelligence contributes to the early detection of pathologies, such as chest pain. In this study, the machine learning techniques were used, performing an analysis of 27 variables provided by a database with information from 258 geriatric patients with 60 years old average age from Medical Norte Hospital in Tijuana, Baja California, Mexico. The objective of this analysis is to determine which variables are correlated with thoracic pain of cardiac origin and use the results as secondary parameters to evaluate the thoracic pain in the emergency rooms, and determine if its origin comes from a CVD or not. For this, two machine learning techniques were used: Tree classification and cross-validation. As a result, the Logistic Regression model, using the characteristics proposed as second factors to consider as variables, obtained an average accuracy (μ) of 96.4% with a standard deviation (σ) of 2.4924, while for F1 a mean (μ) of 91.2% and a standard deviation (σ) of 6.5640. This analysis suggests that among the main factors related to cardiac thoracic pain are: Dyslipidemia, diabetes, chronic kidney failure, hypertension, smoking habits, and troponin levels at the time of admission, which is when the pain occurs. Considering dyslipidemia and diabetes as the main variables due to similar results with machine learning techniques and statistical methods, where 61.95% of the patients who suffer an Acute Myocardial Infarction (AMI) have diabetes, and the 71.73% have dyslipidemia.

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This article proposes a framework to design and implement e- Health interventions in a comprehensive manner. We draw on complexity science to study the interplay of the ecosystem, the behavior and interactions among its agents. We provide a platform to estimate the Quality of Experience (QoE) to assess the relationship between technology and human factors involved in e-Health projects. Our aim is to estimate QoE in e-Health ecosystems from the perspective of complexity by adopting a methodology that uses fuzzy logic to study the behavior of the ecosystem’s agents. We apply the proposed framework to a remote diagnosis case by means of an ultrasound probe through a satellite link. Despite the ambiguities for determining QoE, the experiment demonstrates the applicability of the framework and allows to stressing the importance of human factors in the implementation of e-Health projects.

The emergence of digital convergence and the explosive growth of wireless communications in conjunction with learning experiences regarding the provision of Internet services in rural communities all over the world make necessary to rethinking the strategies and methods employed by governments, development agencies and private sectors to detonate socioeconomic development through the adoption of Information and Communication Technologies (ICT) in rural communities. We start our analysis from experiences in our participation in digital inclusion projects as well as from reports and research papers recently published that show that the availability of broadband infrastructure, convergent devices for Internet access and associated content, though necessary, it is not enough to accomplish sustainable social and economic development in rural communities. We suggest that it is necessary to explore new perspectives, which in conjunction with the cyber-infrastructure, consider two complementary programs; a social action program to incorporate strategies for community participation, technology adoption, usability and capacity building and a program for social innovation to encourage entrepreneurship and organizational development in the rural context. We pose that the cyberinfrastructure and associated programs constitute a socio-technical system interacting with the rural context, which in turn we characterize as a socio-environmental system. We argue that the project objectives should arise as a result of the stakeholders' interaction of both systems in the operational, tactic and strategic levels.

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